Mobility in urban and interurban areas, mainly by cars, is a day-to-day activity of many people. However, some of its main drawbacks are traffic jams and accidents. Newly made vehicles have pre-installed driving evaluation systems, which can prevent accidents. However, most cars on our roads do not have driver assessment systems. In this paper, we propose an approach for recognising driving styles and enabling drivers to reach safer and more efficient driving. The system consists of two physical sensors connected to a device node with a display and a speaker. An artificial neural network (ANN) is included in the node, which analyses the data from the sensors, and then recognises the driving style. When an abnormal driving pattern is detected, the speaker will play a warning message. The prototype was assembled and tested using an interurban road, in particular on a conventional road with three driving styles. The gathered data were used to train and validate the ANN. Results, in terms of accuracy, indicate that better accuracy is obtained when the velocity, position (latitude and longitude), time, and turning speed for the 3-axis are used, offering an average accuracy of 83%. If the classification is performed considering just two driving styles, normal and aggressive, then the accuracy reaches 92%. When the geo-information and time data are included, the main novelty of this paper, the classification accuracy is improved by 13%.

翻译：城市及城际区域机动出行（主要以小汽车为主）是许多人日常活动的一部分，然而交通拥堵和事故是其面临的主要问题。新型车辆虽预装驾驶评估系统以预防事故，但道路上大多数车辆并未配备驾驶员评估系统。本文提出一种识别驾驶风格的方法，旨在帮助驾驶员实现更安全、更高效的驾驶。该系统由两个物理传感器连接至一个带显示屏和扬声器的设备节点组成。节点内置人工神经网络（ANN），通过分析传感器数据识别驾驶风格。当检测到异常驾驶模式时，扬声器会播放警告信息。原型系统已在城际道路（特别是具有三种驾驶风格的常规道路）上完成组装与测试。收集的数据用于训练和验证ANN。精度结果表明，当使用速度、位置（经纬度）、时间以及三轴转向速度作为输入特征时，平均精度可达83%；若仅将驾驶风格分为正常与激进两类，精度可达92%。本文主要创新在于引入地理信息与时间数据后，分类精度提升13%。